Method for Dynamically Assigning Question Priority Based on Question Extraction and Domain Dictionary

ABSTRACT

An approach is provided dynamically prioritizing question requests based on extracted question data. In the approach, performed by an information handling system, a number of question requests to a question and answering (QA) system are received from a computer network, and a plurality of question priority parameters are identified, including one or more question topics and a plurality question context parameters, by performing natural language processing (NLP) analysis of each question request. The approach determines a target priority value for each question request based on the plurality of question priority parameters identified for said question request. By evaluating the target priority values for the plurality of question requests, processing of the question requests is prioritized, such as by applying an artificial intelligence (AI) learned models and rule-based logic at the information handling system to evaluate the target priority values for the plurality of question requests.

BACKGROUND OF THE INVENTION

In the field of artificially intelligent computer systems capable ofanswering questions posed in natural language, cognitive questionanswering (QA) systems (such as the IBM Watson™ artificially intelligentcomputer system or and other natural language question answeringsystems) process questions posed in natural language to determineanswers and associated confidence scores based on knowledge acquired bythe QA system. In operation, users submit one or more questions througha front-end application user interface (UI) or application programminginterface (API) to the QA system where the questions are processed togenerate answers that are returned to the user(s). When a large numberof users are simultaneously submitting multiple questions (e.g.,thousands of questions at any given time), traditional QA systems treatevery question with the same level of priority in terms ofprioritization so that the questions are processed in chronologicalorder, but this can lead to inefficient allocation of the QA systemresources, such as can occur when single “noisy neighbor” user asksmultiple, narrowly focused questions directed to a subset of theingested corpus, resulting in poor or uneven response processing by theQA system for other questions that might have similar or higherimportance. While certain question prioritization schemes have beenproposed which use one or more ad-hoc static priority field valuesassociated with the incoming request to perform prioritization, suchschemes are not applicable to the processing of natural languagequestions due to the non-deterministic nature of such questions. As aresult, the existing solutions for efficiently prioritizing andprocessing questions are extremely difficult at a practical level.

SUMMARY

Broadly speaking, selected embodiments of the present disclosure providea system, method, and apparatus for dynamically prioritizing theprocessing of inquiries to an information handling system capable ofanswering questions by using the cognitive power of the informationhandling system to perform natural language processing (NLP) analysis oneach question and identify named entity and question context informationwhich provide feature values used by a question priority manager toassign a target question priority value to each question. In selectedembodiments, the information handling system may be embodied as aquestion answering (QA) system which receives a set of questions fromone or more users. For each question, the QA system determines aquestion topic and associated question urgency value by performing NLPanalysis to identify the question topic in terms of named entities,n-grams, phrases, and/or terms in the question, and also comparing themwith one or more domain dictionaries (for the domain in which the QAsystem is operating), and by extracting question context informationfrom the question. In selected embodiments, the extracted questioncontext information includes, but is not limited to, data thatidentifies the question timing (e.g., date and time), question source(e.g., user ID, source device, channel, etc.), question originationlocation (e.g., hospital, public-safety answering point, or otherspecial interest location identifier), and the like. The QA system mayalso determine selected operational metrics data for the processing ofeach question, including but not limited to available resources,operational or run-time data for the QA system (e.g., CPU utilization,available disk space, bandwidth utilization, etc.), existing processingrequirements under Service Level Agreement (SLA) and/or Quality ofService (QoS) commitments, history of analysis and feedback for thequestion or submitting user, and the like. Using the question topic,question context information, and/or operational metric data to populatefeature values of a priority assignment module, the QA system may use anartificial intelligence (AI) learned and rule-based logic to determineand assign a question urgency value to each question for purposes ofprioritizing the response processing of each question by the QA system.Using the question topic and feature values to weight the questionpriority, the resulting question urgency value may be used todynamically prioritize response processing by the QA system, such as byusing question urgency values to sort question processing or includingthe question urgency value with each question.

The foregoing is a summary and thus contains, by necessity,simplifications, generalizations, and omissions of detail; consequently,those skilled in the art will appreciate that the summary isillustrative only and is not intended to be in any way limiting. Otheraspects, inventive features, and advantages of the present invention, asdefined solely by the claims, will become apparent in the non-limitingdetailed description set forth below.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention may be better understood, and its numerousobjects, features, and advantages made apparent to those skilled in theart by referencing the accompanying drawings, wherein:

FIG. 1 depicts a network environment that includes a knowledge managerthat utilizes a knowledge base;

FIG. 2 is a block diagram of a processor and components of aninformation handling system such as those shown in FIG. 1;

FIG. 3 illustrates a simplified flow chart showing the logic fordynamically assigning question priority based on extracted named entityand question context information; and

FIG. 4 illustrates an example output of a question priority managerwhich processes a data set to generate feature values which are used bya machine-learning predictive model to assign a target question priorityvalue to each question.

DETAILED DESCRIPTION

The present invention may be a system, a method, and/or a computerprogram product. In addition, selected aspects of the present inventionmay take the form of an entirely hardware embodiment, an entirelysoftware embodiment (including firmware, resident software, micro-code,etc.) or an embodiment combining software and/or hardware aspects thatmay all generally be referred to herein as a “circuit,” “module” or“system.” Furthermore, aspects of the present invention may take theform of computer program product embodied in a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a dynamic or static random access memory(RAM), a read-only memory (ROM), an erasable programmable read-onlymemory (EPROM or Flash memory), a magnetic storage device, a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Java, Smalltalk, C++ or the like,and conventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server or cluster of servers. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

FIG. 1 depicts a schematic diagram of one illustrative embodiment of aquestion prioritization system 10 and question/answer (QA) system 100connected to a computer network 102. The QA system 100 includes aknowledge manager 104 that is connected to a knowledge base 106 andconfigured to provide question/answer (QA) generation functionality forone or more content users who submit across the network 102 to the QAsystem 100. To assist with efficient sorting and presentation ofquestions to the QA system 100, the prioritization system 10 may beconnected to the computer network 102 to receive user questions, and mayinclude a plurality of subsystems which interact with cognitive systems,like the knowledge manager 100, to prioritize questions or requestsbeing submitted to the knowledge manager 100.

The Named Entity subsystem 12 receives and processes each question 11 byusing natural language (NL) processing to analyze each question andextract question topic information contained in the question, such asnamed entities, phrases, n-grams, urgent terms, and/or other specifiedterms which are stored in one or more domain entity dictionaries 13. Byleveraging a plurality of pluggable domain dictionaries relating todifferent domains or areas (e.g., travel, healthcare, electronics, gameshows, financial services), the domain dictionary 11 enables criticaland urgent words (e.g., “threat level”) from different domains (e.g.,“travel”) to be identified in each question based on their presence inthe domain dictionary 11. To this end, the Named Entity subsystem 12 mayuse a Natural Language Processing (NLP) routine to identify the questiontopic information in each question. As used herein, “NLP” refers to thefield of computer science, artificial intelligence, and linguisticsconcerned with the interactions between computers and human (natural)languages. In this context, NLP is related to the area of human-computerinteraction and natural language understanding by computer systems thatenable computer systems to derive meaning from human or natural languageinput. For example, NLP can be used to derive meaning from ahuman-oriented question such as, ““What is tallest mountain in NorthAmerica?” and to identify specified terms, such as named entities,phrases, or urgent terms contained in the question. The processidentifies key terms and attributes in the question and compares theidentified terms to the stored terms in the domain dictionary 13.

The Question Priority Manager subsystem 14 performs additionalprocessing on each question to extract question context information 15A.In addition or in the alternative, the Question Priority Managersubsystem 14 may also extract server performance information 15B for thequestion prioritization system 10 and/or QA system 100. In selectedembodiments, the extracted question context information 15A may includedata that identifies the user context and location when the question wassubmitted or received. For example, the extracted question contextinformation 15A may include data that identifies the user who submittedthe question (e.g., through login credentials), the device or computerwhich sent the question, the channel over which the question wassubmitted, the location of the user or device that sent the question,any special interest location indicator (e.g., hospital, public-safetyanswering point, etc.), or other context-related data for the question.The Question Priority Manager subsystem 14 may also determine or extractselected server performance data 15B for the processing of eachquestion. In selected embodiments, the server performance information15B may include operational metric data relating to the availableprocessing resources at the question prioritization system 10 and/or QAsystem 100, such as operational or run-time data, CPU utilization data,available disk space data, bandwidth utilization data, etc. As part ofthe extracted information 15A/B, the Question Priority Manager subsystem14 may identify the SLA or QoS processing requirements that apply to thequestion being analyzed, the history of analysis and feedback for thequestion or submitting user, and the like. Using the question topicinformation and extracted question context and/or server performanceinformation, the Question Priority Manager subsystem 14 is configured topopulate feature values for the Priority Assignment Model 16 whichprovides a machine learning predictive model for generating a targetpriority values for the question, such as by using an artificialintelligence (AI) based learned models and in some cases rule-basedlogic to determine and assign a question urgency value to each questionfor purposes of prioritizing the response processing of each question bythe QA system 100.

The Prioritization Manager subsystem 17 performs additional sort or rankprocessing to organize the received questions based on at least theassociated target priority values such that high priority questions areput to the front of a prioritized question queue 18 for output asprioritized questions 19. In the question queue 18 of the PrioritizationManager subsystem 17, the highest priority question is placed at thefront for delivery to the assigned QA system 100. In selectedembodiments, the prioritized questions 19 from the PrioritizationManager subsystem 17 that have a specified target priority value may beassigned to a specific pipeline (e.g., QA System 100A) in the QA systemcluster 100. As will be appreciated, the Prioritization Managersubsystem 17 may use the question queue 18 as a message queue to providean asynchronous communications protocol for delivering prioritizedquestions 19 to the QA system 100 such that the Prioritization Managersubsystem 17 and QA system 100 do not need to interact with a questionqueue 18 at the same time by storing prioritized questions in thequestion queue 18 until the QA system 100 retrieves them. In this way, awider asynchronous network supports the passing of prioritized questionsas messages between different computer systems 100A, 100B, connectingmultiple applications and multiple operating systems. Messages can alsobe passed from queue to queue in order for a message to reach theultimate desired recipient. An example of a commercial implementation ofsuch messaging software is IBM's WebSphere MQ (previously MQ Series). Inselected embodiments, the organizational function of the PrioritizationManager subsystem 17 may be configured to convert over-subscribingquestions into asynchronous responses, even if they were asked in asynchronized fashion.

The QA system 100 may include one or more QA system pipelines 100A,100B, each of which includes a computing device 104 (comprising one ormore processors and one or more memories, and potentially any othercomputing device elements generally known in the art including buses,storage devices, communication interfaces, and the like) for processingquestions received over the network 102 from one or more users atcomputing devices (e.g., 110, 120, 130) connected over the network 102for communication with each other and with other devices or componentsvia one or more wired and/or wireless data communication links, whereeach communication link may comprise one or more of wires, routers,switches, transmitters, receivers, or the like. In this networkedarrangement, the QA system 100 and network 102 may enablequestion/answer (QA) generation functionality for one or more contentusers. Other embodiments of QA system 100 may be used with components,systems, sub-systems, and/or devices other than those that are depictedherein.

In each QA system pipeline 100A, 100B, a prioritized question 19 isreceived and prioritized for processing to generate an answer 20. Insequence, prioritized questions 19 are dequeued from the shared questionqueue 18, from which they are de-queued by the pipeline instances forprocessing in priority order rather than insertion order. In selectedembodiments, the question queue 18 may be implemented based on a“priority heap” data structure. During processing within a QA systempipeline (e.g., 100A), questions may be split into many subtasks whichrun concurrently. A single pipeline instance can process a number ofquestions concurrently, but only a certain number of subtasks. Inaddition, each QA system pipeline may include a prioritized queue (notshown) to manage the processing order of these subtasks, with thetop-level priority corresponding to the time that the correspondingquestion started (earliest has highest priority). However, it will beappreciated that such internal prioritization within each QA systempipeline may be augmented by the external target priority valuesgenerated for each question by the Question Priority Manager subsystem14 to take precedence or ranking priority over the question start time.In this way, more important or higher priority questions can “fasttrack” through the QA system pipeline if it is busy with already-runningquestions.

In the QA system 100, the knowledge manager 104 may be configured toreceive inputs from various sources. For example, knowledge manager 104may receive input from the question prioritization system 10, network102, a knowledge base or corpus of electronic documents 106 or otherdata, a content creator 108, content users, and other possible sourcesof input. In selected embodiments, some or all of the inputs toknowledge manager 104 may be routed through the network 102 and/or thequestion prioritization system 10. The various computing devices (e.g.,110, 120, 130) on the network 102 may include access points for contentcreators and content users. Some of the computing devices may includedevices for a database storing the corpus of data as the body ofinformation used by the knowledge manager 104 to generate answers tocases. The network 102 may include local network connections and remoteconnections in various embodiments, such that knowledge manager 104 mayoperate in environments of any size, including local and global, e.g.,the Internet. Additionally, knowledge manager 104 serves as a front-endsystem that can make available a variety of knowledge extracted from orrepresented in documents, network-accessible sources and/or structureddata sources. In this manner, some processes populate the knowledgemanager with the knowledge manager also including input interfaces toreceive knowledge requests and respond accordingly.

In one embodiment, the content creator creates content in a document 106for use as part of a corpus of data with knowledge manager 104. Thedocument 106 may include any file, text, article, or source of data(e.g., scholarly articles, dictionary definitions, encyclopediareferences, and the like) for use in knowledge manager 104. Contentusers may access knowledge manager 104 via a network connection or anInternet connection to the network 102, and may input questions toknowledge manager 104 that may be answered by the content in the corpusof data. As further described below, when a process evaluates a givensection of a document for semantic content, the process can use avariety of conventions to query it from the knowledge manager. Oneconvention is to send a well-formed question. Semantic content iscontent based on the relation between signifiers, such as words,phrases, signs, and symbols, and what they stand for, their denotation,or connotation. In other words, semantic content is content thatinterprets an expression, such as by using Natural Language (NL)Processing. In one embodiment, the process sends well-formed questions(e.g., natural language questions, etc.) to the knowledge manager.Knowledge manager 104 may interpret the question and provide a responseto the content user containing one or more answers to the question. Insome embodiments, knowledge manager 104 may provide a response to usersin a ranked list of answers.

In some illustrative embodiments, QA system 100 may be the IBM Watson™QA system available from International Business Machines Corporation ofArmonk, N.Y., which is augmented with the mechanisms of the illustrativeembodiments described hereafter. The IBM Watson™ knowledge managersystem may receive an input question which it then parses to extract themajor features of the question, that in turn are then used to formulatequeries that are applied to the corpus of data. Based on the applicationof the queries to the corpus of data, a set of hypotheses, or candidateanswers to the input question, are generated by looking across thecorpus of data for portions of the corpus of data that have somepotential for containing a valuable response to the input question.

The IBM Watson™ A system then performs deep analysis on the language ofthe input prioritized question 19 and the language used in each of theportions of the corpus of data found during the application of thequeries using a variety of reasoning algorithms. There may be hundredsor even thousands of reasoning algorithms applied, each of whichperforms different analysis, e.g., comparisons, and generates a score.For example, some reasoning algorithms may look at the matching of termsand synonyms within the language of the input question and the foundportions of the corpus of data. Other reasoning algorithms may look attemporal or spatial features in the language, while others may evaluatethe source of the portion of the corpus of data and evaluate itsveracity.

The scores obtained from the various reasoning algorithms indicate theextent to which the potential response is inferred by the input questionbased on the specific area of focus of that reasoning algorithm. Eachresulting score is then weighted against a statistical model. Thestatistical model captures how well the reasoning algorithm performed atestablishing the inference between two similar passages for a particulardomain during the training period of the IBM Watson™ QA system. Thestatistical model may then be used to summarize a level of confidencethat the IBM Watson™ QA system has regarding the evidence that thepotential response, i.e., candidate answer, is inferred by the question.This process may be repeated for each of the candidate answers until theIBM Watson™ QA system identifies candidate answers that surface as beingsignificantly stronger than others and thus, generates a final answer,or ranked set of answers, for the input question. The QA system 100 thengenerates an output response or answer 20 with the final answer andassociated confidence and supporting evidence. More information aboutthe IBM Watson™ QA system may be obtained, for example, from the IBMCorporation website, IBM Redbooks, and the like. For example,information about the IBM Watson™ QA system can be found in Yuan et al.,“Watson and Healthcare,” IBM developerWorks, 2011 and “The Era ofCognitive Systems: An Inside Look at IBM Watson and How it Works” by RobHigh, IBM Redbooks, 2012.

Types of information handling systems that can utilize QA system 100range from small handheld devices, such as handheld computer/mobiletelephone 110 to large mainframe systems, such as mainframe computer170. Examples of handheld computer 110 include personal digitalassistants (PDAs), personal entertainment devices, such as MP3 players,portable televisions, and compact disc players. Other examples ofinformation handling systems include pen, or tablet, computer 120,laptop, or notebook, computer 130, personal computer system 150, andserver 160. As shown, the various information handling systems can benetworked together using computer network 102. Types of computer network102 that can be used to interconnect the various information handlingsystems include Local Area Networks (LANs), Wireless Local Area Networks(WLANs), the Internet, the Public Switched Telephone Network (PSTN),other wireless networks, and any other network topology that can be usedto interconnect the information handling systems. Many of theinformation handling systems include nonvolatile data stores, such ashard drives and/or nonvolatile memory. Some of the information handlingsystems may use separate nonvolatile data stores (e.g., server 160utilizes nonvolatile data store 165, and mainframe computer 170 utilizesnonvolatile data store 175). The nonvolatile data store can be acomponent that is external to the various information handling systemsor can be internal to one of the information handling systems. Anillustrative example of an information handling system showing anexemplary processor and various components commonly accessed by theprocessor is shown in FIG. 2.

FIG. 2 illustrates information handling system 200, more particularly, aprocessor and common components, which is a simplified example of acomputer system capable of performing the computing operations describedherein. Information handling system 200 includes one or more processors210 coupled to processor interface bus 212. Processor interface bus 212connects processors 210 to Northbridge 215, which is also known as theMemory Controller Hub (MCH). Northbridge 215 connects to system memory220 and provides a means for processor(s) 210 to access the systemmemory. In the system memory 220, a variety of programs may be stored inone or more memory device, including a question priority module 221which may be invoked to dynamically assigning question priority based onquestion extraction and domain dictionary. Graphics controller 225 alsoconnects to Northbridge 215. In one embodiment, PCI Express bus 218connects Northbridge 215 to graphics controller 225. Graphics controller225 connects to display device 230, such as a computer monitor.

Northbridge 215 and Southbridge 235 connect to each other using bus 219.In one embodiment, the bus is a Direct Media Interface (DMI) bus thattransfers data at high speeds in each direction between Northbridge 215and Southbridge 235. In another embodiment, a Peripheral ComponentInterconnect (PCI) bus connects the Northbridge and the Southbridge.Southbridge 235, also known as the I/O Controller Hub (ICH) is a chipthat generally implements capabilities that operate at slower speedsthan the capabilities provided by the Northbridge. Southbridge 235typically provides various busses used to connect various components.These busses include, for example, PCI and PCI Express busses, an ISAbus, a System Management Bus (SMBus or SMB), and/or a Low Pin Count(LPC) bus. The LPC bus often connects low-bandwidth devices, such asboot ROM 296 and “legacy” I/O devices (using a “super I/O” chip). The“legacy” I/O devices (298) can include, for example, serial and parallelports, keyboard, mouse, and/or a floppy disk controller. Othercomponents often included in Southbridge 235 include a Direct MemoryAccess (DMA) controller, a Programmable Interrupt Controller (PIC), anda storage device controller, which connects Southbridge 235 tononvolatile storage device 285, such as a hard disk drive, using bus284.

ExpressCard 255 is a slot that connects hot-pluggable devices to theinformation handling system. ExpressCard 255 supports both PCI Expressand USB connectivity as it connects to Southbridge 235 using both theUniversal Serial Bus (USB) the PCI Express bus. Southbridge 235 includesUSB Controller 240 that provides USB connectivity to devices thatconnect to the USB. These devices include webcam (camera) 250, infrared(IR) receiver 248, keyboard and trackpad 244, and Bluetooth device 246,which provides for wireless personal area networks (PANs). USBController 240 also provides USB connectivity to other miscellaneous USBconnected devices 242, such as a mouse, removable nonvolatile storagedevice 245, modems, network cards, ISDN connectors, fax, printers, USBhubs, and many other types of USB connected devices. While removablenonvolatile storage device 245 is shown as a USB-connected device,removable nonvolatile storage device 245 could be connected using adifferent interface, such as a Firewire interface, etc.

Wireless Local Area Network (LAN) device 275 connects to Southbridge 235via the PCI or PCI Express bus 272. LAN device 275 typically implementsone of the IEEE 802.11 standards for over-the-air modulation techniquesto wireless communicate between information handling system 200 andanother computer system or device. Extensible Firmware Interface (EFI)manager 280 connects to Southbridge 235 via Serial Peripheral Interface(SPI) bus 278 and is used to interface between an operating system andplatform firmware. Optical storage device 290 connects to Southbridge235 using Serial ATA (SATA) bus 288. Serial ATA adapters and devicescommunicate over a high-speed serial link. The Serial ATA bus alsoconnects Southbridge 235 to other forms of storage devices, such as harddisk drives. Audio circuitry 260, such as a sound card, connects toSouthbridge 235 via bus 258. Audio circuitry 260 also providesfunctionality such as audio line-in and optical digital audio in port262, optical digital output and headphone jack 264, internal speakers266, and internal microphone 268. Ethernet controller 270 connects toSouthbridge 235 using a bus, such as the PCI or PCI Express bus.Ethernet controller 270 connects information handling system 200 to acomputer network, such as a Local Area Network (LAN), the Internet, andother public and private computer networks.

While FIG. 2 shows one information handling system, an informationhandling system may take many forms, some of which are shown in FIG. 1.For example, an information handling system may take the form of adesktop, server, portable, laptop, notebook, or other form factorcomputer or data processing system. In addition, an information handlingsystem may take other form factors such as a personal digital assistant(PDA), a gaming device, ATM machine, a portable telephone device, acommunication device or other devices that include a processor andmemory.

FIGS. 3-4 depict an approach that can be executed on an informationhandling system to prioritize questions being presented to a knowledgemanagement system, such as QA system 100 shown in FIG. 1. This approachcan be included within the QA system 100 or provided as a separatequestion prioritization system 10 that is interposed between thecomputer network 102 and the QA system 100. Wherever implemented, thedisclosed question prioritization scheme mines submitted questions forinformation to obtain an improved understanding of the entirety of thequestion before assessing the question's priority. The mined informationincludes the presence of any key terms, phrases, or named entities inthe question which may be determined by using NLP techniques to extractand compare the question terms with terms stored in one or more domaindictionaries which store significant words, names, or phrases indifferent industry areas. In addition, the prioritization schemeextracts contextual information for each question, such as the locationor device where the question was submitted, the identity or profile ofthe user submitting the question, and even the system context underwhich the question was asked, (e.g., operational metric data for theprocessing of each question) which can affect the prioritization of thequestion. The prioritization scheme may also leverage key businessperformance indicators into processing priority, such as SLA or QoSrequirements. Those skilled in the art will appreciate the applicabilityof the question prioritization approach in a deep question answeringsystem where a plurality of different questions might be posed bydifferent users in using varying phrasing. For example, users may submitquestions, such as “What is the number to reach the Air Ambulance?” or“Does the local pharmacy provide a first aid kit?” or “What is thecurrent terror threat level?” or “How should I use the defibrillator?”Based on the extracted key terms, question context, and/or systemcontext under which the question are asked, the questions can beprioritized using the techniques and approaches described herein.

To provide additional details for an improved understanding of selectedembodiments of the present disclosure, reference is now made to FIG. 3which depicts a simplified flow chart showing the logic for dynamicallyassigning question priority based on extracted named entity and questioncontext information. The processing shown in FIG. 3 is performed when aquestion request (e.g., question 11) is presented for processing, suchas by a cognitive system, such as an IBM Watson™ QA system or othernatural language question answering system shown in FIG. 1. FIG. 3processing commences at 301 whereupon, at step 302, the process analyzesthe question request using NLP techniques to identify key questionattributes and terms that are included in the question request. Theprocessing at step 302 may be performed at the question prioritizationsystem 10, QA system 100, or other NLP question answering system. Asdescribed herein, a Natural Language Processing (NLP) routine may beused to perform deep NLP analysis on the question request, where “NLP”refers to the field of computer science, artificial intelligence, andlinguistics concerned with the interactions between computers and human(natural) languages. In this context, NLP is related to the area ofhuman-computer interaction and natural language understanding bycomputer systems that enable computer systems to derive meaning fromhuman or natural language input. For example, NLP can be used to derivemeaning from a human-oriented question such as, “What is the number toreach the Air Ambulance?” and return a listing of one or more key termsincluded in the question request.

At step 304, urgent words or phrases are extracted from the questionrequest. The processing at step 304 may be performed at the Named Entitysubsystem 12 (FIG. 1) or other NLP routine which processes or triagesthe question request to extract urgent words or phrases contained in thequestion, such as named entity, relationship, and/or co-referenceinformation contained in the question request. For example, in a firstquestion request—“What is the number to reach the Air Ambulance?”—theextracted named entity information may be the term “Air Ambulance,”while the extracted relationship information may refer to therelationship between air ambulance (named entity), number (noun) andreach (verb), and the extracted co-reference information may refer tothe (phone) number to contact the air ambulance. In contrast, a secondquestion request—“Does the local pharmacy provide a first aid kit?—mightinclude extracted named entity information, “pharmacy,” which wouldmerit a lower priority assessment as compared to the extracted “AirAmbulance” term from the first question request. As a result of theextraction processing step 304, one or more priority attribute featuresfor the question request may be identified and quantified, such as aquestion identification (e.g., QuestionID) or date attribute feature forthe question request (e.g., Date=11/19/2013) or a word attribute featurefor the question request (e.g., Word=abnormal or critical or fire orpain or effective, etc.).

At step 306, origin information for the question request is extractedfrom the question request. The processing at step 304 may be performedat the Named Entity subsystem 12 and/or Question Priority Manager 14(FIG. 1) or other NLP routine. In selected embodiments, the extractedorigin information may be geographical or location information relatingto the question request (e.g., a 911 call center, hospital, or otherspecified location). In addition or in the alternative, the extractedorigin information may include geographical or location information forthe user device which submitted the question request (e.g., the GPSlocation information for a cell phone or a communication channel overwhich the question request was presented, the IP address for anoriginating device, etc.). In addition or in the alternative, theextracted origin information may include geographical or locationinformation for a user group which submitted the question request (e.g.,an identifier for a customer group associated with question request,such as a hospital group having a specified SLA or QoS agreement). As aresult of the origin processing step 306, one or more priority attributefeatures for the question request may be identified and quantified, suchas an origin location attribute feature for the question request (e.g.,OriginLocation=PSAP or Precinct, or Federal Location or Other) or anorigin device attribute feature for the question request (e.g.,OriginDevice=Mozilla/5.0 (iPad) or Mozilla/5.0 (iPhone) or Mozilla/5.0(Android) or Window NT 6.1).

At step 308, the extracted urgent words from the question request may becompared or checked against a domain entity dictionary. By leveraging adomain dictionary (e.g., Domain Dictionary 13), the processing step 308may can compare extracted urgent words or phrases from the questionrequest with a plurality of different dictionaries for different domainsor areas (e.g., travel, healthcare, electronics, game shows, financialservices). The results of the processing step 308 may be used to assigna higher priority level to the question request which contains termsthat are also stored in the domain entity dictionary. The processing atstep 308 may be performed at the Named Entity subsystem 12 and/orQuestion Priority Manager 14 (FIG. 1) or other NLP routine. As a resultof the domain dictionary check processing step 308, one or more priorityattribute features for the question request may be identified andquantified to indicate whether extracted words in the question requestcorrespond to terms in a domain dictionary, such as an domain dictionaryattribute feature for the question request (e.g.,PresentInDomainDict=True or False).

At step 310, the question request may be analyzed to determine if theextracted origin location information corresponds to a special interestlocation (such as a hospital, public safety answering point, or otherspecified location) which is slated for higher priority treatment. Atprocessing step 310, the extracted origin location information may bechecked against certain specified locations having assigned prioritylevels which are used to elevate or increase the prioritization of thequestion request. In addition or in the alternative, the extractedorigin location information may be checked against dynamicallyidentified origin locations (e.g., regions where there is a poweroutage, tornado, or other incident of interest). The results of theprocessing step 310 may be used to assign a higher priority level to thequestion request. The processing at step 310 may be performed at theNamed Entity subsystem 12 and/or Question Priority Manager 14 (FIG. 1)or other NLP routine. As a result of the special interest locationprocessing step 310, one or more priority attribute features for thequestion request may be identified and quantified to indicate whetherthe question request originated from a special interest location (e.g.,SpecialInterestLocation=True or False).

At step 312, the question request may be analyzed to assess the priorityof the user or user group who originated or submitted the questionrequest. For example, if profile information for the originating user oruser group indicates that the user is entitled to higher prioritytreatment under the processing requirements of Service Level Agreement(SLA) and/or Quality of Service (QoS) commitments, the results of theprocessing step 312 may be used to elevate or increase theprioritization of the question request. The processing at step 312 maybe performed at the Named Entity subsystem 12 and/or Question PriorityManager 14 (FIG. 1) or other NLP routine. As a result of the userpriority processing step 312, one or more priority attribute featuresfor the question request may be identified and quantified to indicatethe priority of the user or user group that submitted the questionrequest (e.g., OriginGroup=Management or Administrator or Agent orCustomer or Unknown).

At step 314, the question request may be analyzed by retrievingoperational performance-related data, such as operational run-time data,for the server systems which are available to answer the questionrequest. Examples of such retrieved performance data include, but arenot limited to CPU utilization rates, available disk space, bandwidthutilization at the question answering system. If the retrievedoperational performance data indicates that a first QA system isoverloaded, the results of the processing step 314 will be used toassign the question request to another QA system or instance. Theprocessing at step 314 may be performed at the Named Entity subsystem 12and/or Question Priority Manager 14 (FIG. 1) or other NLP routine. As aresult of the processing step 314, one or more priority attributefeatures for the question request may be identified and quantified toindicate the operational performance-related data for the applicable QAsystem (e.g., CPUUtil=15.299% or 89.55%).

At step 316, the process uses the combined results of the previous steps302-314 to run feature values through a priority prediction machinelearning model to generate a priority value for the question request. Inselected embodiments, the processing at step 316 may be performed atQuestion Priority Manager 14 (FIG. 1) or other NLP routine which invokesthe Priority Assignment Model 16 for each question request usingassociated feature values extracted from the question request and otherplaces. In response to the populated feature values, the priorityprediction model generates a target variable (e.g., T_Priority=Escalatedor Low or Normal or High) for the question request which may be assignedas a target priority value to the question request. To illustrate anexample result of the processing step 316, reference is now made to FIG.4 which depicts an example screen shot output 400 from the questionpriority manager which processes a data set for a specified questionrequest 401 to generate values 404 for a plurality of priority attributefeatures 402 and a corresponding target question priority 403. Asillustrated, the question request 401 is identified with a QuestionIDfield having a specified value. In addition, an associated plurality ofvalues 404 for the priority attribute fields 402 may be generated by thequestion priority manager which extracts, reads, and populates differentfeature or attribute fields 402 (e.g., Date, Word,PresentInDomainDictionary, OriginLocation, OriginDevice, PriorityUser,OriginGroup, SpecialInterestLocationIndicator, CPUUtil, etc.) withcorresponding priority attribute feature values 404. In selectedembodiments, the question priority manager may use a machine-learningpredictive model to assign a target question priority value 404 (e.g.,“Normal” or “High”) as the question priority 403 for the questionrequest based on the question topic, question context information,and/or operational performance metric data for the QA system. Forexample, the question priority may be based on specified named entity orterm phrases extracted from the question and present in a domain entitydictionary, the originating user group name/id, the originatinglocation, the operational run-time data for the system, and overall SLAcommitments on processing time. In evaluating the priority attributevalues, numerical thresholds or categories may be used to assignpriority values using machine learning classification techniques, suchas linear regression, logistic regression, decision trees, and the like.For example, lower priority may be indicated if the number of questionrequests is at or below a specified threshold, while higher priority maybe indicated if the number of question requests is above the specifiedthreshold. Similarly, lower priority may be indicated if the retrievedsystem performance metric (e.g., CPUUtil) is at or below a specifiedperformance threshold, while higher priority may be indicated if theretrieved system performance metric is above the specified performancethreshold. In addition, higher priority may be indicated if theoriginating user group is identified as a priority group (e.g.,OriginGroup=Administrator or Agent or Management), while lower prioritymay be indicated if the originating user group is part of a lowerpriority group (e.g., OriginGroup=Customer or Unknown).

Referring back to FIG. 3, the question request is organized in thequestion queue based on its assigned priority for delivery to the QAsystem at step 318. In selected embodiments, the processing at step 318may be performed at Prioritization Manager 17 (FIG. 1) or other messageprocessing software (e.g., IBM's WebSphere MQ or MQ Series software)which places the question request on a heap for processing by adesignated QA system. The Prioritization Manager 17 may organize thequestion queue by weighing the assigned priority values for the questionrequests such that high priority questions having high-ranking priorityvalues are put to the front of the queue for sequential removal by adesignated QA system pipeline. After prioritization of pending questionrequests, prioritization process ends at step 320, at which point thequestion requests in the question queue may be processed by thedesignated pipeline (e.g., QA System 100A in the QA system cluster 100)which de-queues the highest priority question request. In selectedembodiments, each de-queued question request may include the assignedpriority value. However, in other embodiments, the question requests inthe question queue do not include the assigned priority values.

By now, it will be appreciated that there is disclosed herein a system,method, apparatus, and computer program product for dynamicallyprioritizing questions based on extracted question parameters using aninformation handling system having a processor and a memory. Asdisclosed, the system, method, apparatus, and computer program productreceive a plurality of questions and extract therefrom a plurality ofquestion priority parameters including one or more question topics and aplurality of question context parameters for each question. In selectedembodiments, the question priority parameters may be extracted byperforming a natural language processing (NLP) analysis of eachquestion, wherein the NLP analysis results in one or more questiontopics comprising extracted named entity information from said question.In other embodiments, the question priority parameters may be extractedby comparing the extracted named entity information from each questionto key priority terms contained in one or more domain dictionarydatabases to identify extracted named entity information that matchesone or more key priority terms. In other embodiments, the questionpriority parameters may be extracted by identifying, for each question,question timing data, question source data, question location data,profile data for the user or user group submitting the question andperformance requirement data for the user or user group submitting thequestion. Based on the plurality of question priority parametersidentified for said question, a target priority value is determined foreach question. In selected embodiments, the target priority value may bedetermined by executing a priority prediction machine learning modelagainst the plurality of question priority parameters identified foreach question to generate a priority value for said question. Inaddition, one or more system performance parameters—such as a processorutilization data, available memory space data, and bandwidth utilizationdata—may be identified by the information handling system for use indetermining the target priority value for each question. Aftergenerating the target priority values, the information handling systemmay dynamically prioritize processing of the plurality of questions bycomparing the target priority values for each of the plurality ofquestions. In selected embodiments, processing of the plurality ofquestions may be dynamically prioritized by applying an artificialintelligence (AI) learned models and rule-based logic at the informationhandling system to sort the plurality of questions based on the targetpriority values for the plurality of questions.

While particular embodiments of the present invention have been shownand described, it will be obvious to those skilled in the art that,based upon the teachings herein, that changes and modifications may bemade without departing from this invention and its broader aspects.Therefore, the appended claims are to encompass within their scope allsuch changes and modifications as are within the true spirit and scopeof this invention. Furthermore, it is to be understood that theinvention is solely defined by the appended claims. It will beunderstood by those with skill in the art that if a specific number ofan introduced claim element is intended, such intent will be explicitlyrecited in the claim, and in the absence of such recitation no suchlimitation is present. For non-limiting example, as an aid tounderstanding, the following appended claims contain usage of theintroductory phrases “at least one” and “one or more” to introduce claimelements. However, the use of such phrases should not be construed toimply that the introduction of a claim element by the indefinitearticles “a” or “an” limits any particular claim containing suchintroduced claim element to inventions containing only one such element,even when the same claim includes the introductory phrases “one or more”or “at least one” and indefinite articles such as “a” or “an”; the sameholds true for the use in the claims of definite articles.

1-20. (canceled)
 21. A method of dynamically prioritizing responseprocessing of questions in an information handling system which iscapable of answering questions and which comprises a processor and amemory, the method comprising: determining, by the information handlingsystem, a question topic and associated question urgency value for eachquestion in a set of questions; and dynamically prioritizing, by theinformation handling system, response processing of each questionaccording to an inquiry weighting based on the question topic andassociated question urgency value.
 22. The method of claim 21, whereindynamically prioritizing response processing of each question comprisesapplying, by the information handling system, an artificial intelligence(AI) learned model and rule-based logic to determine a correspondinginquiry weighting for each question.
 23. The method of claim 22, whereinthe corresponding inquiry weighting for each question is determined byextracting operational metric data comprising question timing data,question source data, question origination location data, availableresources data, operational or run-time data, Service Level Agreement(SLA) data, Quality of Service (QoS) commitments data, and history ofanalysis and feedback for the question or submitting user.
 24. Themethod of claim 21, wherein determining the question topic andassociated question urgency value for each question comprisesextracting, by the information handling system, a plurality of questionpriority parameters by performing a natural language processing (NLP)analysis of each question, wherein the NLP analysis results in one ormore question topics comprising extracted named entity information fromsaid question.
 25. The method of claim 24, wherein extracting theplurality of question priority parameters further comprises comparingthe extracted named entity information from each question to keypriority terms contained in one or more domain dictionary databases toidentify extracted named entity information that matches one or more keypriority terms.
 26. The method of claim 21, wherein determining thequestion topic and associated question urgency value for each questioncomprises extracting, by the information handling system, a plurality ofquestion priority parameters by identifying, for each question, questiontiming data, question source data, question location data, profile datafor the user or user group submitting the question and performancerequirement data for the user or user group submitting the question. 27.The method of claim 21, further comprising generating, for eachquestion, a corresponding inquiry weighting by using an artificialintelligence (AI) learned and rule-based logic to determine and assignthe associated question urgency value to each question based on one ormore question topics, question priority parameters, and/or operationalmetric data extracted from each question.
 28. An information handlingsystem comprising: one or more processors; a memory coupled to at leastone of the processors; a set of instructions stored in the memory andexecuted by at least one of the processors to dynamically prioritizeresponse processing of questions, wherein the set of instructionsperform actions of: determining, by the information handling system, aquestion topic and associated question urgency value for each questionin a set of questions; and dynamically prioritizing, by the informationhandling system, response processing of each question according to aninquiry weighting based on the question topic and associated questionurgency value.
 29. The information handling system of claim 28, whereindynamically prioritizing response processing of each question comprisesapplying, by the information handling system, an artificial intelligence(AI) learned model and rule-based logic to determine a correspondinginquiry weighting for each question.
 30. The information handling systemof claim 29, wherein the corresponding inquiry weighting for eachquestion is determined by extracting operational metric data comprisingquestion timing data, question source data, question originationlocation data, available resources data, operational or run-time data,Service Level Agreement (SLA) data, Quality of Service (QoS) commitmentsdata, and history of analysis and feedback for the question orsubmitting user.
 31. The information handling system of claim 28,wherein determining the question topic and associated question urgencyvalue for each question comprises extracting, by the informationhandling system, a plurality of question priority parameters byperforming a natural language processing (NLP) analysis of eachquestion, wherein the NLP analysis results in one or more questiontopics comprising extracted named entity information from said question.32. The information handling system of claim 31, wherein extracting theplurality of question priority parameters further comprises comparingthe extracted named entity information from each question to keypriority terms contained in one or more domain dictionary databases toidentify extracted named entity information that matches one or more keypriority terms.
 33. The information handling system of claim 28, whereindetermining the question topic and associated question urgency value foreach question comprises extracting, by the information handling system,a plurality of question priority parameters by identifying, for eachquestion, question timing data, question source data, question locationdata, profile data for the user or user group submitting the questionand performance requirement data for the user or user group submittingthe question.
 34. The information handling system of claim 28, furthercomprising generating, for each question, a corresponding inquiryweighting by using an artificial intelligence (AI) learned andrule-based logic to determine and assign the associated question urgencyvalue to each question based on one or more question topics, questionpriority parameters, and/or operational metric data extracted from eachquestion.
 35. A computer program product stored in a computer readablestorage medium, comprising computer instructions that, when executed byan information handling system, causes the information handling systemto dynamically prioritize response processing of questions in aninformation handling system which is capable of answering questions byperforming actions comprising: determining, by the information handlingsystem, a question topic and associated question urgency value for eachquestion in a set of questions; and dynamically prioritizing, by theinformation handling system, response processing of each questionaccording to an inquiry weighting based on the question topic andassociated question urgency value.
 36. The computer program product ofclaim 35, wherein dynamically prioritizing response processing of eachquestion comprises applying, by the information handling system, anartificial intelligence (AI) learned model and rule-based logic todetermine a corresponding inquiry weighting for each question.
 37. Thecomputer program product of claim 36, wherein the corresponding inquiryweighting for each question is determined by extracting operationalmetric data comprising question timing data, question source data,question origination location data, available resources data,operational or run-time data, Service Level Agreement (SLA) data,Quality of Service (QoS) commitments data, and history of analysis andfeedback for the question or submitting user.
 38. The computer programproduct of claim 35, wherein the corresponding inquiry weighting foreach question is determined by extracting operational metric datacomprising question timing data, question source data, questionorigination location data, available resources data, operational orrun-time data, Service Level Agreement (SLA) data, Quality of Service(QoS) commitments data, and history of analysis and feedback for thequestion or submitting user.
 39. The computer program product of claim38, wherein extracting the plurality of question priority parametersfurther comprises comparing the extracted named entity information fromeach question to key priority terms contained in one or more domaindictionary databases to identify extracted named entity information thatmatches one or more key priority terms.
 40. The computer program productof claim 35, wherein determining the question topic and associatedquestion urgency value for each question comprises extracting, by theinformation handling system, a plurality of question priority parametersby identifying, for each question, question timing data, question sourcedata, question location data, profile data for the user or user groupsubmitting the question and performance requirement data for the user oruser group submitting the question.